Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.

Background modeling has been proven to be a promising method of hyperspectral anomaly detection. However, due to the cluttered imaging scene, modeling the background of an hyperspectral image (HSI) is often challenging. To mitigate this problem, we propose a novel structured background modeling-based hyperspectral anomaly detection method, which clearly improves the detection accuracy through exploiting the block-diagonal structure of the background. Specifically, to conveniently model the multi-mode characteristics of background, we divide the full-band patches in an HSI into different background clusters according to their spatial-spectral features. A spatial-spectral background dictionary is then learned for each cluster with a principal component analysis (PCA) learning scheme. When being represented onto those dictionaries, the background often exhibits a block-diagonal structure, while the anomalous target shows a sparse structure. In light of such an observation, we develop a low-rank representation based anomaly detection framework that can appropriately separate the sparse anomaly from the block-diagonal background. To optimize this framework effectively, we adopt the standard alternating direction method of multipliers (ADMM) algorithm. With extensive experiments on both synthetic and real-world datasets, the proposed method achieves an obvious improvement in detection accuracy, compared with several state-of-the-art hyperspectral anomaly detection methods.